We propose a framework to compute the optimal routes for multi-drones to minimize the delivery time in the last-mile delivery service. We mainly focus on a notion of the landing exclusion zone that appears during the landing phase; an area around the drop-off site is blocked until a drop-off is completed. Such zones affect the delivery time as other drones need to detour or hover around the site unnecessarily. We formulate the Mixed-Integer Linear Programming (MILP) problem by explicitly modeling the landing phase. Then, we present the heuristic algorithm that iteratively solves a sequence of single-drone delivery problems according to the delivery priorities. A delivery priority is determined according to the spatiotemporal occupancy that quantifies the significance of the size of the landing exclusion zone and its blocking period. We designed the experiment for 48 urban delivery scenarios with varying density and distribution of delivery destinations, departure points, and order quantities. Our experiment results show that the heuristic computes the routes significantly faster than the original MILP, and the delivery time is 5% higher from the optimal solution (lower-bound), and 60% lower from the general requirement of a single package per round-trip (upper-bound).

The Robotic Mobile Fulfillment System (RMFS) is an automated technology to fulfill various types of orders (e.g., vehicle assembly) in modern warehouses or factories. In particular, battery-equipped mobile robots move racks that contain various components (Stock Keeping Unit) by visiting replenishment or picking stations to complete orders. We formulate the preliminary system model to optimize the order throughput and energy consumption of mobile robots, which consists of (i) rack allocation, (ii) task allocation, and (iii) route routing.

To enhance the performance of autonomous driving, recent studies have been incorporating various tasks that require increasingly more computation. As computational demands increase, it is often difficult to achieve timely execution with the limited performance of onboard computing units alone. To address this issue, Vehicle Edge Computing (VEC), which offloads computational workloads to the edge and retrieves the results back to the vehicle, is gaining significant attention. To achieve efficient offloaded analytics via VEC, it is crucial to comprehensively consider both of the computing and network conditions of the V2X systems, as well as the vehicle energy consumption and timely execution. However, current studies have not sufficiently addressed the comprehensive modeling of computational and network loads in these V2X systems. To deal with this, we propose a Cooperative Network-Computation Load Balancing Simulator for VEC.

Side objects are the common road objects placed alongside roadways, such as traffic signs, trees or street lights. Even though such objects do not obstruct a driving path directly, the visual perception-based autonomous features may recognize them in an unintended way impeding their ideal behavior. We propose a framework that systematically places various types of side objects in the virtual environment for testing the perception-based steering control. Firstly, we give the mathematical constraints that characterize both linear and non-linear geometric aspects in placing the side objects including the distances from a roadway or other side objects, and their placement patterns. Secondly, we define the placement distribution criteria that characterize how well the side objects are to be distributed along the roadways. Finally, our placement generation algorithm automatically determines the position of the side objects via the SMT (Satisfiability Modulo Theories) solver, and the generated placements are guaranteed to conform to both the constraints and distribution criteria. The experiment shows the scalability of the placement algorithm as to how fast a large number of side objects can be generated conforming to the aforementioned properties. In addition, we show how the Convolutional Neural Network (CNN)-based steering controller alters its behavior under multiple environments generated from our framework according to the RMSE and disengagement metrics.

With increasing connectivity and sophisticated software, modern vehicles are able to leverage different kinds of services provided by the environment. One such service recommended by the Automotive Edge Computing Consortium (AECC) is downloading high-definition map data by vehicles. This high volume of data can be provided to the vehicles when moving by pre-allocating resources on edge server nodes or roadside units if the routes are known apriori. However, this is not a realistic assumption to make in general. Therefore, in this work, we propose a two-stage optimization framework for efficient data delivery to connected vehicles via edge nodes while considering dynamic route changes. We have evaluated the efficiency of this proposed approach (considering a real-world dataset) with respect to (a) offline optimization strategies considering fixed routes and (b) a greedy approach considering route changes. Our proposed approach works considerably better than the existing approaches in the context of dynamic route changes.

With the rapid advancements in communication technologies, the paradigm of connected vehicles is drastically transforming the automotive industry, enabling efficient data and service delivery to vehicles via edges. Various works have considered data delivery without a Medium Access Layer (MAC), which can result in multiple data frame collisions in the network. The time-slot-based MAC layer strategy uses slot assignment to ensure collision-free data delivery for multiple vehicles across various transmission channels at each edge. However, the increasing requests from various vehicular nodes can increase network congestion, thus servicing fewer vehicles. In the current work, we propose an optimization framework for collision-aware data delivery considering two state-of-art MAC protocols, HCCA and VeMAC. The proposed framework minimizes the global slot utilization cost for edge-to-vehicle data delivery, considering vehicle flow, edge resources, and vehicle overlaps while avoiding possible data transmission collisions. Further, we demonstrate the practicality of the framework in terms of the number of vehicles served, global slot utilization cost, and bandwidth cost. We further analyze the framework with differences in vehicle densities for various problem sizes using a real-world traffic scenario.

Today very few deep learning-based mobile augmented reality (MAR) applications are applied in mobile devices because they are significantly energy-guzzling. In this paper, we design an edge-based energy-aware MAR system that enables MAR devices to dynamically change their configurations, such as CPU frequency, computation model size, and image offloading frequency based on user preferences, camera sampling rates, and available radio resources. Our proposed dynamic MAR configuration adaptations can minimize the per frame energy consumption of multiple MAR clients without degrading their preferred MAR performance metrics, such as latency and detection accuracy. To thoroughly analyze the interactions among MAR configurations, user preferences, camera sampling rate, and energy consumption, we propose, to the best of our knowledge, the first comprehensive analytical energy model for MAR devices. Based on the proposed analytical model, we design a LEAF optimization algorithm to guide the MAR configuration adaptation and server radio resource allocation. An image offloading frequency orchestrator, coordinating with the LEAF, is developed to adaptively regulate the edge-based object detection invocations and to further improve the energy efficiency of MAR devices. Extensive evaluations are conducted to validate the performance of the proposed analytical model and algorithms.

The emergence of vehicle connectivity technologies and associated applications have paved the way for increased consumer interest in connected vehicles. These modern day vehicles are now capable of sending/receiving vast amounts of data and offloading computation (which is one possible service) to servers thereby improving safety, comfort, driving experience, etc. In the early stages of connectivity, all the data communication and computation offloading happened between the cloud server and the vehicles. However, this is not feasible in scenarios having strict timing requirements and bandwidth cost constraints. Vehicular Edge Computing (VEC) demonstrated an efficient way to tackle the above problem. In order to optimally utilize the resources of the edge servers for data delivery, an efficient edge resource allocation framework needs to be developed. In a recent work, data/service delivery to connected vehicles assumed a worst-case scenario that all vehicles with routes passing through an edge appear in the edge coverage region simultaneously. However, this worst-case scenario is very pessimistic, which results in overestimation of edge resources. We address this by precisely computing the set of vehicles which simultaneously appear in the coverage region of an edge (which we call vehicle overlaps). In this work, we first propose an optimization framework for edge resource allocation that minimizes the bandwidth cost of data delivery to connected vehicles while considering the traffic flow and vehicle overlaps. Then, we propose an efficient heuristic to deliver data based on minimizing global edge bandwidth cost gradient under vehicle overlaps. We demonstrate the improvement in resource allocation considering vehicle overlaps. Using real world traffic data, we also demonstrate reduction in data delivery times using the proposed heuristic.

Autonomous driving needs various line-of-sight sensors to perceive surroundings that could be impaired under diverse environment uncertainties such as visual occlusion and extreme weather. To improve driving safety, we explore to wirelessly share perception information among connected vehicles within automotive edge computing networks. Sharing massive perception data in real time, however, is challenging under dynamic networking conditions and varying computation work-loads. In this paper, we propose LiveMap, a real-time dynamic map, that detects, matches, and tracks objects on the road with crowdsourcing data from connected vehicles in sub-second. We develop the data plane of LiveMap that efficiently processes individual vehicle data with object detection, projection, feature extraction, object matching, and effectively integrates objects from multiple vehicles with object combination. We design the control plane of LiveMap that allows adaptive offloading of vehicle computations, and develop an intelligent vehicle scheduling and offloading algorithm to reduce the offloading latency of vehicles based on deep reinforcement learning (DRL) techniques. We implement LiveMap on a small-scale testbed and develop a large-scale network simulator. We evaluate the performance of LiveMap with both experiments and simulations, and the results show LiveMap reduces 34.1% average latency than the baseline solution.

With the rise in state-of-the-art communication modes for vehicles such as vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to cloud (V2C), modern vehicles are increasingly being connected to cloud and fog/edge nodes. These vehicle connectivity modes have enabled the realization of Vehicular Edge Computing (VEC) paradigm, whereby vehicles can leverage fog/edge node resources for storage/computation. In a VEC system, vehicles receive very important and large quantity of data from edge nodes, which is termed as data delivery. In addition, edge nodes can execute some services and send the results back to the vehicle, which is called service delivery. Fast and efficient edge resource allocation for data/service delivery is important in order to serve as many vehicles as possible in the VEC system. However, edge resource allocation is complex with large number of edges and vehicles, while also considering vehicle flow parameters. In this work, we propose Edge-Pairwise Optimal Data/Service Delivery (E-PODS), which is a fast and efficient heuristic for data/service delivery. Through experiments with synthetic and real vehicular traces, we demonstrate that E-PODS is considerably faster than the optimal approach, while making resource allocations that are close to optimal in terms of total edge bandwidth cost and number of serviced vehicles.

Electric Vehicle (EV) public charging is important to meet the exploding charging demand and to address the range anxiety issue. In this paper, we focus on the EV public charging market with heterogeneous charging stations (CSs) under the time-based billing model. We jointly consider the charging time optimization for EVs, the EV-CS pairing, and the pricing mechanism for CSs. A hierarchical game, which mathematically corresponds to an equilibrium problem with equilibrium constraints (EPEC), is then developed to formulate the three coupled problems. In the proposed hierarchical game, each CS sets the charging price to maximize its own revenue first, then the EVs choose their desired CSs and determine the charging time. We analyze the optimal charging time strategies for EVs, and a many-to-one matching algorithm is applied to solve the EV-CS pairing problem. Besides, a block coordinate descent (BCD) based algorithm is applied for each CS to solve the pricing problem. Simulation results show that our proposed schemes can achieve the performance improvement of the charging system.

Emerging connected vehicle services powered by artificial intelligence and data analytic are gaining increasing interest and attention with the advancement of cloud/edge computing technologies. Given the highly data- and computation-intensive characteristics of these applications, it is important that requests for these services be carefully allocated in cloud/edge computing systems to optimize performance, resource utilization and cost. Challenges arises when mobility of vehicles is taken into consideration. Specifically, as services become increasingly sophisticated and computation intensive, a vehicle may travel non-trivial amount of distance during queuing and processing of a request, which affect transmission of result data upon service fulfillment. In these cases, it is important that allocation of request be aware of the expected position of the vehicle at the time of request completion as oppose to submission. In the general scenario where there exists multiple cloud/edge devices and resource contention, it is important to simultaneously consider vehicle trajectories, workload and scheduling of requests to jointly optimize allocation. In this paper, we study the problem of cost minimization in allocation and scheduling of connected vehicle service requests on heterogeneous cloud/edge services. We consider the scenario where vehicles have non-trivial mobility during service delay and model its impact on data transmission. We introduce an optimal ILP formulation as well as an efficient and close to optimal heuristic algorithm for solving the optimization problem. Experiment result shows that the proposed technique is capable of achieving 10% to 30% of improvement comparing with straightforward approaches.

Connected car is one of the major trends in the auto-motive industry to provide users with advanced services to improve driving quality and safety. For example, a predictive maintenance service identifies any malfunction of vehicles by analyzing sensor data sent from them; High-Definition (HD) map can be constructed by collecting sensor data from multiple vehicles, and distributed to them to enhance the quality of any autonomous features.

Big data disrupts everything it touches, but automotive is probably one of the top industries that enjoy and leverage the benefits. The Automotive Big Data Pipeline (ABDP) is a Big Data pipeline base on the automotive use case and is required to scale up agile and high performance in real-time or in batch. Nonetheless, there're many alternative infrastructure designs but lack of knowledge, which fits the best for the automotive domain. It leads this paper into a question: What kinds of infrastructure design could provide better performance for the ABDP?In this paper, we introduce two well-known infrastructure designs called Hyper-Converged infrastructure (HCI) and Disaggregated Hyper-Converged infrastructure (DHCI). HCI combines standard data center hardware using locally attached storage resources to create fast, common building blocks. However, does single standard hardware fit all the requirements? DHCI scale independently from compute and storage provides an option. It provides a more cost-efficient and flexible solution; however, there is no comparison from the performance point of view. Therefore, to address it, our objective is to conduct an empirical performance comparison to see which one performs better.The experiment result shows that DHCI performs almost the same as HCI on CPU utilization, memory, and network consumption. However, regarding storage and running time metrics, DHCI performs slightly higher storage throughput, IOPs, and less running time than HCI.

The trend of connected/autonomous features adds significant complexity to the traditional automotive systems to improve driving safety and comfort. Engineers are facing significant challenges in designing test environments that are more complex than ever. We propose a test framework that allows one to automatically generate various virtual road environments from the path and behavior specifications. The path specification intends to characterize geometric paths that an environmental object (e.g., a roadway or a pedestrian) needs to be visualized or move over. We characterize this aspect in the form of constraints of 3-Dimensional (3D) coordinates. Then, we introduce a test coverage, called an area coverage, to quantify the quality of the generated paths in terms of how diverse of an area the generated paths can cover. We propose an algorithm that automatically generates such paths using an SMT (Satisfiability Modulo Theories) solver. However, the behavioral specification intends to characterize how an environmental object changes its mode over time by interacting with other objects (e.g., a pedestrian waits for a signal or starts crossing). We characterize this aspect in the form of timed automata. Then, we introduce a test coverage, called an edge/location coverage, to quantify the quality of the generated mode changes in terms of how many modes or transitions are visited. We propose a method that automatically generates many different mode changes using a model-checking. To demonstrate the test framework, we developed the right-turn pedestrian warning system in intersection scenarios and generated many different types of pedestrian paths and behaviors to analyze the effectiveness of the system.

Applying deep learning to object detection provides the capability to accurately detect and classify complex objects in the real world. However, currently, few mobile applications use deep learning because such technology is computation- and energy-intensive. This paper, to the best of our knowledge, presents the first detailed experimental study of the smartphone's energy consumption and the detection latency of executing deep Convolutional Neural Networks (CNN) optimized object detec- tion, either locally on the smartphone or remotely on an edge server. We experiment with a variety of smartphones, obtaining different levels of computation capacities, in order to ensure that we are not profiling a specific device. Our detailed measurements refine the energy analysis of smartphones and reveal some interesting perspectives regarding the energy consumption of executing the deep CNN optimized object detection. We believe that these findings will guide the design of energy efficient processing pipeline of the CNN optimized object detection.

With the rapid advancement of automobile industry, autonomous driving in connected vehicles are expected to be the key technology to satisfy the expansion of human demands on more comfortable and safer driving experience. However, only on-board computation resources are insufficient to satisfy tough computation requirements of achieving full or even high automation. Therefore, autonomous driving with cloud/edge participation is desirable. In this paper, we propose E-Auto, a novel communication scheme to enable fast, stable, and accurate edge-assisted autonomous driving service for connected vehicles within any road types (e.g., driving on highway with very high speed or local roads with slow speed due to traffic congestion). In addition, as two key components of the proposed E-Auto scheme, a service period allocation algorithm and a frame resolution selection algorithm are designed to guarantee a sufficient frame rate for connected vehicles acquiring either uplink application (offload camera captured frames to the edge server) or downlink application (download entertainment videos). Through network simulations, we evaluate the performance of the proposed E-Auto scheme. Simulation results demonstrate that E-Auto can provide a high frame rate and low energy consumption autonomous driving service for connected vehicles.

A growing number of vehicles are equipped with Vehicle-to-vehicle (V2V) communication modules (e.g., Dedicated Short Range Communications) that allow them to exchange messages over the network. The V2V communication is expected to improve the road safety by overcoming limitation of conventional Advanced Driver Assistance Systems (ADASs). For a safe feature using V2V communication-based applications, it is essential to identify sender vehicles since V2V communication is typically implemented using a broadcast mechanism. Especially here, our focus is to correctly determine whether the preceding vehicle is the sender of the received message or not in order to realize cooperative driving such as platooning. Vehicle location information obtained by an onboard GPS module is typically used for the identification. However, the GPS module often provides wrong location information due to the limited accuracy in a certain environment such as an urban road surrounded by tall buildings. To prevent this GPS error from causing misidentifications, we propose a novel method which additionally uses shared ranging sensor data and behavioral control of the ego vehicle. Simulation result shows that our proposed method successfully reduces the number of misidentifications by 64 % compared with a method which fully depends on GPS information.

Safety-critical embedded systems often need to meet dependability requirements such as strict input/output timing constraints. To meet the timing requirements, the code generation (e.g., C code) from timed models needs to determine the timing parameters that indicate when the code has to perform I/O with its platform. We propose a novel framework to determine such timing parameters from platform-independent timed models. Our framework involves two transformations. The first transformation systematically extends the platform-independent model by explicitly modeling input/output processing (e.g., sampling or interrupt-based) and the code invocation (e.g., periodic or aperiodic) mechanisms. Then, we verify if the resulting platform-specific model meets the timing requirements. In the case that the resulting model does not satisfy the timing requirements, we apply the second transformation to compensate the platform delay via adjusting the timing parameters at the code level. We formulate the adjustment mechanism using integer linear programming. If such an adjustment is feasible, generating the code with the new timing parameters guarantees the implemented system to meet the timing requirements. We validate our framework with case studies running on Patient-Controlled Analgesia (PCA) infusion pump platforms.

The paradigm of connected vehicles is fast gaining lot of attraction in the automotive industry. Recently, a lot of technological innovation has been pushed through to realize this paradigm using vehicle to cloud (V2C), infrastructure (V2I) and vehicle (V2V) communications. This has also opened the doors for efficient delivery of data/service to the vehicles via edge devices that are closer to the vehicles. In this work, we propose an optimization framework that can be used to deliver data/service to the connected vehicles such that a bandwidth cost objective is optimized. For the first time, we also integrate a vehicle flow model in the optimization framework to model the traffic flow in the coverage area of the edges. Using the optimization framework, we study the variation of the optimal bandwidth cost for varying problem sizes and vehicle flow model parameter values for both data and service delivery.

Computation offloading over fog and cloud is critical to improve service quality and efficiency of future networks. Mobile vehicles have also been considered as potential fog nodes by sparing their computation capability to nearby users. In this paper, we propose a multi-layer computation offloading architecture, consisting of the user layer, mobile fog layer, fixed fog layer and cloud layer. Multiple wireless roadside units (RSUs) are deployed in the network to collect computation tasks from user layer, and offload the tasks to other layers. Each layer has distinct multi-dimensional characteristics, such as different transmission rates and computation capabilities. The computation tasks may consume different communication and computation resources when they are uploaded to different layers. However, the available resources of each layer are limited. Consider that each user will pay for the offloaded computation tasks according to their sizes, we aim to maximize the total profits of computation offloading from the infrastructure perspective. Specifically, the offloading problem is formulated as a generalized multidimensional multiple knapsack problem (MMKP), in which each layer is considered as a large knapsack and the computation tasks are treated as items. We propose a modified branch-and-bound algorithm to obtain the optimal solution, and a heuristic greedy method to obtain approximate performance with much lower computational overhead. A comprehensive simulation is conducted to compare the proposed two algorithms. Simulation results demonstrate that the proposed computation offloading architecture together with the task allocation algorithms can achieve the purpose of maximizing the total profits of offloaded tasks.

Vehicle simulators are widely used to test the correctness of vehicle control algorithms. It is important to create a virtual road environment in a way that the vehicle algorithm can be tested under various circumstances that may happen in the real world. However, building such a road environment is typically time consuming and performed in a manual and ad-hoc fashion without having a good notion of coverage criteria. We propose the automatic road network generation for vehicle simulation that is based on Satisfiability Modulo Theories (SMT). We first introduce the curve coverage criteria to characterize the property of horizontal/vertical curves that are required to test advanced safety features such as adaptive cruise control or lane keeping assistance. This criteria includes the number of curves, the distance of adjacent curves and horizontal/vertical curvatures. We propose the road network generation algorithms that utilize the SMT solver to determine a set of 3 dimensional coordinates. This algorithm takes an input of the parametrized constraints formalized from the curve coverage criteria and automatically determines a set of 3D coordinates to generate the road structure. Vehicle simulation engines can then use these coordinates to visualize the road networks, and such road networks are guaranteed to conform to the curve coverage criteria. We developed a plug-in for the Unity3D simulation engine that automates this process and demonstrate the applicability of the generated the road network for the adaptive cruise control testing.

Optional software-based features are increasingly becoming an important cost driver in automotive systems. These include features pertaining to active safety, infotainment, etc. Currently, these optional features are integrated into the vehicles at the factory during assembly. This severely restricts the flexibility of the customer to select and use features on-demand and therefore, the customer will either have to be satisfied with an available set of feature options or pre-order a car with the required features from the manufacturer resulting in considerable delay. In order to increase flexibility and reduce the delay, it is necessary to provide the option to configure the vehicle on-demand at the dealership or remotely. In this paper, we present our vision and challenges involved in developing a platform infrastructure that allows on-demand deployment of automotive safety features and ensures their correct execution.

Model-based testing of embedded real-time systems is challenging because platform-specific details are often abstracted away to make the models amenable to various analyses. Testing an implementation to expose non-conformance to such a model requires reconciling differences arising from these abstractions. Due to stateful behavior, naive comparisons of model and system behaviors often fail causing numerous false positives. Previously proposed approaches address this by being reactively permissive: passing criteria are relaxed to reduce false positives, but may increase false negatives, which is particularly bothersome for safety-critical systems. To address this concern, we propose an automated approach that is proactively adaptive: test stimuli and system responses are suitably modified taking into account platform-specific aspects so that the modified test when executed on the platform-specific implementation exercises the intended scenario captured in the original model-based test. We show that the new framework eliminates false negatives while keeping the number of false positives low for a variety of platform-specific configurations.

Many safety-critical real-time embedded systems need to meet stringent timing constraints such as preserving delay bounds between input and output events. In model-based development, a system is often implemented by using a code generator to automatically generate source code from system models, and integrating the generated source code with a platform. It is challenging to guarantee that the implemented systems preserve required timing constraints, because the timed behavior of the source code and the platform is closely intertwined. In this paper, we address this challenge by proposing a model transformation approach for the code generation. Our approach compensates the platform-processing delays by adjusting the timing parameters in system models, based on an Integer Linear Programming problem formulation. We demonstrate the usefulness of our approach via a case study of infusion pump systems. Experimental results show that the code generated using our approach can better preserve the timing constraints.

The model-based implementation is to derive an implementation from a model that has been shown to meet requirements. Even though this approach can be used to guarantee that an implementation satisfies functional requirements that are shown to be correct at the model level, it is still challenging to assure timing requirements at the implementation level. We propose a layered approach in testing timing requirements conformance of implemented systems developed by model-based implementation. In our approach, the abstraction boundary of the implemented system is formally defined using Parnas' four-variables model. Then, the proposed approach tests timing aspects of the interaction between the auto-generated code and the target platform-dependent code based on the four-variables. This approach aims at not only detecting the timing requirement violation, but also at measuring delay-segments that contribute to the timing deviation of the implemented system w.r.t. the model. We show the case study of testing timing requirements of an infusion pump system to illustrate the applicability of the proposed framework.

Code generation for embedded systems is challenging, since the generated code (e.g., C code) is expected to run on a heterogeneous set of target platforms with different characteristics, such as hardware/software architectures and programming interfaces. We propose a code generation framework that provides the flexibility to generate different source code that is executable on each target platform. In our framework, the platform-dependent characteristics of a target platform are explicitly specified by an Architectural Analysis Description Language (AADL) model and a code snippet repository. The AADL model captures hardware/software architectural aspects of the platform, such as periodic/aperiodic threads and their interactions with sensors and actuators. The code snippet repository contains platform-dependent code snippets that are categorized according to the functions required to implement the components of the AADL model. These two elements of the platform capability are then used by the code generation algorithm to generate platform-dependent code for the given platform. We demonstrate the applicability of our framework using a case study of code generation for two infusion pump systems.

In model-based development, executable software (e.g., C or Java code) can be generated from a high-level model using a code generator. However, the execution of the generated software on a target platform remains a challenge due to a mismatch in communication semantics assumed by the model and the platform-dependent software (e.g., sampling/actuation routines). This paper proposes an input/output (I/O) interface module that bridges this semantic gap by means of buffers and interface policies, which explicitly capture the information required to adapt the model's communication semantics to that of the platform. We present a framework that can be used to systematically synthesize - directly from the model - the I/O interfaces and accompanying APIs that the generated software and the platform-dependent software need to communicate with one another. Our interface policies can also encode relaxations of a model semantics that may not be implementable, thus making derivations of the implemented systems from the model traceable. We illustrate the applicability and the benefits of our framework with a case study of an infusion pump.

Medical cyber-physical systems (MCPS) are life-critical, context-aware, networked systems of medical devices. These systems are increasingly used in hospitals to provide high-quality continuous care for patients. The need to design complex MCPS that are both safe and effective has presented numerous challenges, including achieving high assurance in system software, intoperability, context-aware intelligence, autonomy, security and privacy, and device certifiability. In this paper, we discuss these challenges in developing MCPS, some of our work in addressing them, and several open research issues.

This paper presents our effort of using model-driven engineering to establish a safety-assured implementation of Patient-Controlled Analgesic (PCA) infusion pump software based on the generic PCA reference model provided by the U.S. Food and Drug Administration (FDA). The reference model was first translated into a network of timed automata using the UPPAAL tool. Its safety properties were then assured according to the set of generic safety requirements also provided by the FDA. Once the safety of the reference model was established, we applied the TIMES tool to automatically generate platform-independent code as its preliminary implementation. The code was then equipped with auxiliary facilities to interface with pump hardware and deployed onto a real PCA pump. Experiments show that the code worked correctly and effectively with the real pump. To assure that the code does not introduce any violation of the safety requirements, we also developed a testbed to check the consistency between the reference model and the code through conformance testing. Challenges encountered and lessons learned during our work are also discussed in this paper.

We introduce a ubiquitous sensor network system, called U-FIPI, to monitor the early symptom of fire and intruder in indoor environment. Especially, this system adapts a novel approach to provide bidirectional location- awareness service between various mobile nodes and U-FIPI fixture nodes making it suitable for total service as a new concept of building management system.